{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Bunch of imports \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import scipy.special\n",
    "import os.path\n",
    "import pickle\n",
    "import ipywidgets\n",
    "from IPython.display import display\n",
    "# import zsharp as zs\n",
    "\n",
    "import math\n",
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Load saved profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data = sio.loadmat('FigS2A.mat',squeeze_me=True)\n",
    "Detuning_grid=Data['Detuning_grid']\n",
    "TTilde_grid=Data['TTilde_grid']\n",
    "TransRate_mat=Data['TransRate_mat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FigS2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 237.5x237.5 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# colormap:bwr\n",
    "\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "pp = PdfPages('Fig2A.pdf')\n",
    "\n",
    "fig = plt.figure(figsize=[2.375,2.375])\n",
    "ax=fig.add_axes([0.23,0.15,0.75,0.6])\n",
    "ax_cb=fig.add_axes([0.23,0.77,0.75,0.025])\n",
    "plt.sca(ax)\n",
    "plt.pcolormesh(Detuning_grid,TTilde_grid,TransRate_mat,cmap='bwr')\n",
    "# plt.pcolormesh(Detuning_grid_0716,TTilde_grid_0716,TransRate_mat_0716,)\n",
    "plt.axvline(5,color=[0.2,0.2,0.2],linewidth=2,alpha=0.8)\n",
    "\n",
    "plt.ylabel('$T/T_F$',fontsize=9,labelpad=0)\n",
    "plt.xlabel('$\\omega/ 2\\pi$ [k$Hz$]',fontsize=9,labelpad=0)\n",
    "\n",
    "ax.tick_params(axis='both',direction='in',labelsize=7)\n",
    "\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='horizontal',ticks=[0.2, 0.4])\n",
    "\n",
    "ax_cb.set_xlabel('$N_f / N_0$',fontsize=9,labelpad=-5)\n",
    "ax_cb.xaxis.set_label_position('top') \n",
    "ax_cb.xaxis.set_ticks_position('top')\n",
    "\n",
    "ax_cb.tick_params(axis='both',direction='in',labelsize=7,length=1.5)\n",
    "\n",
    "\n",
    "pp.savefig(fig)\n",
    "pp.close()"
   ]
  },
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   "outputs": [],
   "source": []
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